| # Supplementary Videos |
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| This directory contains supplementary videos for the anonymous submission. |
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| ## S1: TCP4D Tracking |
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| `S1_TCP4D_Tracking/` contains examples of TCP tracking. Each example includes camera-view videos and corresponding point-cloud visualizations. The pcd video shows the predicted point cloud. |
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| ## S2: Augmented Data |
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| `S2_augumented_data/` contains example videos organized by source: `DROID/`, `RoboTwin/`, and `Robolab/`. |
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| - `original.mp4` and `source_demo_aloha-agilex.mp4` show source demonstrations. |
| - Other filenames identify target robot embodiments, including Franka Panda, Jaco, UR5 with WSG, ARX, and Piper. Files marked `cross_embodiment` show cross-embodiment examples. |
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| All videos were processed using our cross-embodiment pipeline. |
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| ## S3: Real-World Task Demonstrations |
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| `S3_real_task_demo/` contains videos for two real-world tasks. Filenames beginning with `task1` and `task2` correspond to the respective tasks; files with the `_long` suffix show longer versions. |
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| - `task1_fast.mp4` and `task1_long.mp4`: *placing pens* |
| - `task2_fast.mp4` and `task2_long.mp4`: *stacking bowls* |
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| Task 1 shows rollouts for *placing pens*, while Task 2 shows rollouts for *stacking bowls*. |
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| TCP4D infer 32 action step for a chunk, and replan when execute 20 steps. |
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| The `_fast` variant uses the cuRobo library with CUDA acceleration for inverse kinematics (IK) planning. For safety, we adjusted the action execution frequency, so its theoretical speed can be higher than shown. |
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| The `_long` variant includes a joint movement penalty in the optimization objective to reduce large changes in joint positions during execution. It is implemented using SciPy without CUDA acceleration. |
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| ## S4: Few_shot_Adaptation |
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| These two sets of videos both demonstrate the effect of transfer learning using ten-shot training. The videos are all unseen, only for evaluation.. |
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